Score Visualizations: A Visual Exploration Tool for Sheet Music
Georgia Institute of Technology
Atlanta, Georgia, USA
Figure 1: Sample visualizations.
Musicians often use online repositories to nd sheet music. How-
ever, it can be challenging and time consuming to nd pieces with
the desired instrumentation, mood, tempo, diculty, and other
features. In addition, score previews and audio recordings are not
always available before purchasing the sheet music. Because of
these challenges, users may have to visit multiple sites to gather all
the information they need prior to purchasing a score, and/or gravi-
tate towards composers they are already familiar with or those
highlighted in search results, potentially creating bias towards
well-known composers. Our user research suggests that the text-
dependent ltering tools on existing websites may be under-utilized.
In this study we explored a visual-based approach to create a more
intuitive and user-friendly experience.
In this paper we introduce a web-based interactive tool to explore
a repository of scores based on features of the music. We created a vi-
sualization for each piece representing multiple high-level features
(tempo, mode, note density, time signature, instrumentation, and
diculty level) in a single image. Each musical feature, calculated
from the symbolic score representation using music21, maps to a
dierent visual element (color, shape, icons, and pattern density).
Users interact with the tool by selecting from a set of visualizations,
each representing one piece. For the selected piece, users can view
and download the score, listen to a MIDI version, and listen to an
audio recording in an embedded Spotify player.
We discuss the tool’s relevance for users browsing digital music
collections as well as possible issues with implementing such a tool
at scale. We also discuss the challenges that arise from comparing
pieces that vary widely in terms of instrumentation and style.
•Information systems →Data management systems
centered computing →Interaction design;Visualization.
visualization, interfaces, digital music repositories, search tools
Musicians, music educators, and conductors often rely on online
databases to obtain sheet music. These online sources became
even more important in 2020, when traditional sources for printed
sheet music such as brick-and-mortar music stores and publisher
booths at conferences suddenly became more limited because of
the COVID-19 pandemic. There are several free or low-cost sheet
sites such as MuseScore
, as well as publishers’ web-
sites and sheet music distributors such as J.W. Pepper
searching through these websites to nd an appropriate piece can
be challenging. Each site has its own search and ltering features
and may contain thousands of scores, but nding a score with the
desired instrumentation, style, tempo, and other features can be
dicult and time consuming.
There are several challenges contributing to this problem. First,
the user may experience a “cold start” problem. When a search
returns thousands of scores, guring out where to start can be
overwhelming. Second, it can be dicult to nd information about
a piece before purchasing or downloading the sheet music. On
some sites, score previews may not be available before purchasing.
Even if a preview is available, it can take a considerable amount of
time to review a score to determine if it ts the user’s needs. Third,
recommendation and similarity comparison tools, while common
in the audio domain, are not widely available for scores.
Because of these challenges of searching for scores, users may
gravitate toward composers they are already familiar with. Some
sites such as MuseScore highlight the most popular composers/artists,
inuencing users’ browsing choices. This could introduce bias, with
popular composers continuing to be promoted as lesser-known com-
posers are buried in the search results. Users may also gravitate
toward composers whose work they are already familiar with. If
the user nds a piece they like, it can be dicult to nd a similar
piece by a dierent composer because of the lack of similarity and
recommendation tools. The text-based ltering options available
on most sites include features such as time period, instrumentation,
and sometimes diculty level. However, our user research suggests
these lters may be under-utilized. In addition, there are few lter-
ing options available for other features of the music such as tempo,
time signature, and mood.
To avoid reliance on text-based lters and allow the user to
focus on musical features when exploring scores, we approached
the problem from a visual perspective. From a user experience
perspective, visuals are often preferred over excessive text. Our
goal is to allow the user to compare features of pieces at at a glance,
determining quickly whether the piece is of interest to them and
meets their needs.
To test this visual-based approach, we created a visualization of
each piece from a repository of scores, representing classical music
written for a variety of instruments. Aiming for a simple abstract
visual style, we mapped high-level features of scores such as tempo,
time signature, and instrumentation to visual design elements such
as color, shape, and icons. We then created an interactive tool to
navigate a corpus of scores using these visualizations.
Potential use cases for this tool include: 1) categorization and
organization of score databases, 2) improving the user experience
of searching for scores, and 3) promoting lesser-known composers
by focusing on the features of a score rather than the popularity of
2.1 Music Data Visualization
In a recent survey paper, Kulusi et al [
] described the dierent
ways in which visualizations have been used for musical data, fo-
cusing on “non-traditional” abstract representations that have been
used for specic tasks or research questions. Some of the open
problems the authors described are 1) the need for interdisciplinary
collaborations with graphic art and visual design and 2) a lack of
“collection-wide analysis tools for musical scores,” as most corpus
tools tend to focus on audio data. These open problems are areas
we will explore with this project.
There have been many design approaches used to visualize scores
with abstract colors and shapes. However, most of these tend to
be complex and display individual notes or phrases. For example,
Martin Wattenberg’s Arc Diagram [
], uses concentric arcs to
illustrate patterns. The piano roll-style visualizations of Stephen
Malinowski’s Animation Machine
use a video format to display
the visualizations synchronized to the audio. Nicholas Rougeaux’s
visualizations created for OpenScore
display every note in a piece
using a static image, with lower notes in the center and higher
pitches at the edges of the circle. These images convey detailed
information about a piece, whereas a simpler visualization of higher-
level features would be more eective for the purpose of browsing
a database and comparing multiple pieces.
Several visualizations or visual-based interfaces have been de-
signed for organizing and categorizing music, but they have gen-
erally focused on audio features. For example, MusicRainbow[
allows the user scroll through a collection of music displayed as
a circular rainbow, where tracks with similar audio features are
grouped together on the color wheel. Similarly, Jiajun and Lu’s [
interface allows users to navigate a database of audio tracks, using
scatter plot of perceived tempo (based on average tempo and other
features such as average onset duration) and timbre (bright to dark).
These tools have a similar goal to ours in terms of interactive navi-
gation of a database of music visually. However, to our knowledge,
there have not been any similar methods applied to scores.
2.2 Musical and Visual Features
Although there has been extensive research on audio similarity,
Schedl et al [
] discussed the importance of overlooked user-
centric methods in music information retrieval, noting that human
perception of similarity is inuenced by a wide range of individual
factors. When humans are asked to rate audio similarity subjec-
tively, the highly variable results suggests that people focus on dif-
ferent features when assessing similarity, as evidenced by Flexer’s
] ndings from the MIREX study. This indicates that it impor-
tant to include multiple musical features in our visualizations, to
account for the multiple ways in which users may perceive pieces
to be similar - for example, some may focus mainly on tempo, while
others focus more on instrumentation.
Several studies have suggested some natural perceptual map-
pings of visualizations to musical or audio features, and these map-
pings are sometimes tied to emotional associations. For example,
participants who were asked to draw visual representations of mu-
sical stimuli often used visual height to represent frequency and
line thickness to represent amplitude (Küssner [
]). Sievers et al
] demonstrated that the spectral centroid of audio stimuli was
associated with the characteristics of shapes and with emotional
arousal (high centroid sounds were associated with spiky shapes
and high-arousal emotions such as anger, and low centroid sounds
with rounded shapes and low-arousal emotions). Color has also
been shown to have associations with musical features. For exam-
ple, Palmer et al [
] showed that faster music in a major key was
associated with more saturated, lighter, yellow colors, and slower
music in a minor key was associated with less saturated, darker,
bluer colors. The associations were mediated by the participants’
emotional associations with the music. These ndings suggests that
color could be employed as a meaningful representation of features
that represent the emotion of a piece, such as the tempo and mode.
Score Visualizations: A Visual Exploration Tool for Sheet Music
Several prior studies inuenced our decisions regarding the gen-
eral appearance and style of our visualizations. For example, Ver-
schael et al [
] noted that children use a wide variety of visual
elements such as color, brightness, words, and pictographs to repre-
sent music, and we sought to incorporate several of these elements
into our images. Tan & Kelley[
], in one of the few studies to exam-
ine visual representations of complete musical compositions, found
that most participants chose to use abstract visualizations, rather
than pictorial images (70% vs. 30%). Musically trained participants
(the target users for our tool) were more likely to use abstract forms
than musically untrained participants. Taken together, these nd-
ings suggest that a suitable design approach for score visualization
could incorporate abstract shapes and colors, using a variety of
visual design elements to represent dierent features. Rather than
showing detailed changes over time in a continuous left to right
stream (the style used by many of the participants reported by Tan
& Kelley), we decided a global notation approach (as described by
Verschael) was more appropriate for our purposes. To aid in quick
similarity comparison between pieces, we chose to use a single
overall image for each piece.
In the studies mentioned above, while certain representations
were found to be more common than others, there was considerable
variability and subjectivity between participants. Although we did
not discover any “universal” mappings based on the literature, we
used the more consistent of these ndings as guidelines for cre-
ating our visualizations. We chose to use an abstract visual style,
with a single image representing the entire piece, as opposed to
showing changes over time. We also chose to incorporate icons to
represent certain features. The studies cited above also inuenced
our color palette selection, for example using brighter yellow colors
to indicate a happy mood and darker blue colors to indicate a sad
2.3 Diculty Calculation
One of the most important features for selecting a piece is the di-
culty level, which must be consistent with the musicians’ skill level.
However, diculty levels are generally by publishers or expert
reviewers after a manual review process, and there is no single set
of guidelines for diculty level across countries states, or ensemble
types. Andrews [
] notes that there is no consensus across publish-
ers on the criteria for dierent grade levels. Based on a synthesis of
guidelines from dierent publishers along with input from educa-
tors and conductors, Andrews developed a Music Complexity Chart.
This chart contains criteria for classifying music as easy, medium,
or hard for concert band, orchestra, or chamber ensemble. The
level-specic criteria included categories such as range, rhythm,
and piece duration. We drew from this chart to select features that
are important for assessing diculty. However, the guidelines are
not complete for our purposes, as they are subject to interpretation
by human reviewers, and they do not extend to solo pieces or other
instrumentation such as piano or choral music.
To our knowledge, the research on assessing diculty compu-
tationally from symbolic score data is very limited. Chiu & Chen
] Sébastien et al [
] each proposed methods of calculating the
diculty level of piano music, in MIDI and MusicXML formats,
respectively. Both studies included some features that were piano-
specic, such as ngering, hand displacement, and hand stretch.
Polyphony, or number of simultaneous voices, is not relevant for
monophonic instruments and would not have the same meaning in
terms of diculty for larger ensembles. However, they also included
features that could be extended to other instruments. For example
Chiu dened as average playing speed based on the tempo and note
durations, and Sébastien dened a similar measure by the tempo
and shortest signicant note value. Both studies incorporated a
measure of the relative frequency of accidentals. Other measures
included the length of the piece.
Sawruk and Walls [
] presented a system for searching digi-
tal sheet music developed for the J.W. Pepper website. Although
this tool only uses instrument part ranges as a proxy for diculty,
it is notable in its use of visual-based methods for nding sheet
music and its use of symbolic scores to extract that diculty infor-
mation. Easy, medium, and hard total ranges and tessituras were
dened for each instrument. Users select from visual presentations
of these ranges to lter by instrument-specic diculty, allowing
customization of diculty by part.
3 USER RESEARCH
To nd out more about users’ experiences searching for music, we
conducted 10 semi-structured interviews. Participants included am-
ateur and professional musicians, composers and arrangers, a music
therapist, and several music educators (band and orchestra conduc-
tors, a high school band director, a state band chair, and a piano
teacher). We asked about topics including experiences with existing
websites, challenges and frustrations, and important criteria for
We analyzed the results using an anity mapping approach,
grouping important points into several key themes as shown in
Figure 2. We found that participants are using a variety of tools
to nd sheet music, including distributors, free websites, libraries,
and composers’ and publishers’ websites. They reported having
limited time to spend searching, but many are using an inecient
process, visiting multiple websites to nd the information they are
looking for. One reason for this is the inability to view the score
and/or listen to a recording before purchasing or downloading,
prompting participants to search other sites for that information.
Other downsides to existing sites included the inability to search or
lter on features such as tempo or length, and free sites not being
visually appealing or user friendly. If participants were considering
a particular piece or composer and wanted to nd something similar,
they might go to the composer’s website or listen to Spotify playlists
of an ensemble that had performed the piece. Participants did not
report using ltering options on sheet music websites very often.
Instead, they tended to get ideas for pieces from sources such as
Youtube, then multiple sites to nd out where they could download
or purchase the piece.
4.1 Data Processing
Figure 3 provides an overview of our data processing steps. Our
data source is a classical corpus of Kern les in symbolic format,
representing the medieval through modern eras (approximately
Figure 2: User Interview Anity Map. Our interview analysis revealed common themes, with notable ndings indicated in
1150 through 1950). Although we used Kern les for this analy-
sis, the process could be expanded easily to accommodate other
symbolic formats such as musicXML. We sampled one work per
composer, for a total of 58 pieces. If there was any missing or in-
complete information such as instrumentation or tempo, we edited
the Kern les manually to add those elds. We created a list of
works in .csv format, including the specic le names to include
and documentation of any edits made from the original le. We
also searched for the pieces in Spotify and inserted the Spotify ID
for each piece when available. Using Verovio Humdrum viewer, we
created one-page previews of the scores in .png format.
We used music21, a Python toolkit for computer-aided musicol-
ogy, to calculate a set of features for each piece and export them
library for creative coding, to draw the visualizations on the screen.
We used Midi.js to generate audio from the MIDI les.
From our user interviews and literature review, we identied a set
of musical features important for comparing and selecting scores:
diculty, instrumentation, tempo, estimated note density, time sig-
nature, mood, and diculty level. We used music21 to calculate fea-
tures for each piece, as described in Table 1. We identied the tempo,
time signature, and mode at the beginning of the piece. Although
this is an oversimplication because these features can change dur-
ing a piece, using a single value at the start of the piece allows for
a simpler visualization. We categorized instruments present in the
scores into instrument families (woodwinds, piano or harpsichord,
brass, strings, and vocals).
Next we calculated a composite diculty measure from 9 fea-
ture components. These captured information from the following
categories: speed (tempo, note density, and proportion of notes
lasting <=0.25 seconds); key signature (number of sharps or ats,
proportion of accidentals); meter (indicators for compound meter
and meter changes); part-specic range; and the total duration of
Score Visualizations: A Visual Exploration Tool for Sheet Music
Figure 3: Data Flow. The diagram illustrates the data process-
ing steps, from the source les to feature calculation to the
nal interactive tool.
the piece in minutes. To create the composite measure, we rst
scaled each component to a range of [0,1]. Then we summed the
scaled components for each piece. Finally, we categorized each
piece into a ve-point rating scale based on the quintiles of the
4.3 Visual Mappings
Informed by prior research on visual representations of music, as
described in the Introduction, we mapped a dierent visual feature
to each musical feature. We selected four color palettes to represent
mood, as dened by the tempo and mode, roughly corresponding
to a valence/arousal model (Figure 4. The color brightness and
saturation indicated tempo (arousal), with bright, saturated colors
indicating faster tempos of >=100 BPM. The color hue corresponds
to the valence commonly associated with major vs. minor mode.
For example, “happy” (fast and major key) pieces are bright yellow
and green and “sad/melancholy” (slow and minor key) pieces are
less saturated blue and grey.
The shapes used in the background ll represent the time signa-
ture, as shown in Figure 5 (triangles for pieces with a time signature
numerator in 3 or 6, and squares for pieces in 2 or 4). The density
of the background ll corresponds to the average note density, as
shown in Figure 6 (number of notes per second). Icons represent
instrument families (Figure 7), and a ve-point scale indicates the
diculty level (Figure 8).
5 INTERACTIVE FUNCTIONALITY
The tool displays visualizations for seven randomly selected scores
from the database, as shown in Figure 9. One larger image in the cen-
ter is the currently “selected” piece, and six smaller images around
Figure 4: Mood. Color palettes represent mood, dened by
tempo and mode.
Figure 5: Time Signature. Shapes in the background pattern
represent the time signature.
Figure 6: Note density. The density of the background pat-
tern ll corresponds to the note density (average number of
notes per second, taking local tempo into account).
the edges represent other randomly selected scores. The composer
name and piece title are displayed for the current selection only,
a design decision that is intended to focus the user’s attention on
the piece’s features as opposed to the composer’s name. To pick
a dierent piece, the user clicks on of the smaller images, which
Table 1: Music Features
Feature Diculty Measure Component Denition
Time signature Time signature at the beginning of the piece
Mode Major vs. minor mode at the beginning of the piece
Instrument family Instrument families present in the score (e.g. woodwinds, vocals)
Duration ✓Total length of the piece in minutes
Accidentals ✓Proportion of all notes in the piece that are accidentals
Sharps or ats ✓Number of sharps or number of ats in the initial key signature
Range ✓Distance between lowest and highest pitch in semitones for each part
Tempo ✓Initial tempo marking (beats per minute)
Compound meter ✓0/1 indicator for whether time signature is compound meter
Meter changes ✓0/1 indicator for whether there are any meter changes
Note density ✓Average number of notes per second, taking local tempo into account
"Fast" notes ✓Proportion of all notes that have a duration <=0.25 seconds
Figure 7: Instrumentation. Instrument families are repre-
sented using icons.
Figure 8: Diculty level. A ve-point scale illustrates di-
culty level relative to other pieces in the corpus.
becomes the new "current selection", and the smaller images are
re-populated with other pieces from the corpus.
A drop-down gives the user control over the randomization
options. Changing the drop-down from the default "Show random
pieces" to "Show pieces similar to current selection" displays the
pieces that are most similar to the current selection. Similarity is
determined by a point-based system, as described in 2. Each piece
Figure 9: Interactivity design. This screenshot of the web-
based tool shows the layout of the visualizations, buttons
to view the score and listen to audio, and the drop-down to
control randomization options.
in the data set is compared to the current selection, and the tool
displays the six pieces with the highest number of similarity points.
The user can view a one-page preview of the score by clicking the
"view score" button. Additional options are then presented to close
the preview or download it as .jpg le. A "Play MIDI" button enables
play and pause functionality for the MIDI version of the piece. A
"Listen on Spotify" button opens an embedded Spotify player, if
the piece is available on Spotify. If the user is logged into a Spotify
account, they will be able to listen to the entire audio recording.
Otherwise, they will hear a 30-second preview.
Score Visualizations: A Visual Exploration Tool for Sheet Music
Table 2: Similarity Measure
Condition Similarity Points
Same diculty score 2
Diculty within += 1 point 1
Same instrumentation 2
At least one instrument in common 1
Tempo in same range (>=100 vs. <100 BPM) 1
Note density within +- 2 notes per second 1
Same mode (major vs. minor) 1
6 USER TESTING
We administered a follow-up survey to our interview participants,
as well as several email lists and a discussion board for music re-
searchers. This survey asked participants to test out the prototype
and provide feedback on how usable and useful they found the
tool. Preliminary results from six participants indicate that users
found the visual representations to be intuitive, although there may
be some confusion over the meaning of the background pattern
ll. Users liked being able to control the randomization options.
They found the design to be eective overall. Some technical chal-
lenges were reported, including cross-browser compatibility (the
tool works well in Chrome and Edge but not Firefox) and some
issues with the MIDI playback. Users suggested some extensions
and improvements such as adding search and ltering options.
In addition to the survey, we received feedback from two mu-
sic researchers through a discussion board, who suggested some
tweaks to the visualizations such as outlining the shapes in black
to make them more visible. Another suggestion was an alternate
layout allowing the user to explore a larger number of scores at
a time to view, something that was also suggested by one of the
We also solicited feedback on the color palettes used to represent
mood from one (non-musician) individual with red-green color-
blindness. We learned that while some of the mappings were mean-
ingful (e.g., using red to represent "angry/intense" made sense), the
less saturated colors such as the "sad/melancholy" blue and grey
were harder to distinguish and did not carry as much symbolic
meaning. The gure explaining the color palettes (Figure 4) was
also slightly confusing, leading to the expectation that the colors
would be presented as dots on an x-y axis.
The prototype we developed serves as a proof of concept for using
visualizations to explore a database of sheet music, which to our
knowledge has not been done before. Our preliminary user feedback
suggests that users liked the concept and generally were able to
use the tool to explore the repository of scores. It addresses several
issues we identied in our background research, including 1) having
score previews, MIDI, and audio playback easily accessible within
the tool, 2) enabling the user to compare pieces by features such as
time signature, mood, and diculty level. Rather than using text-
based lters and/or relying on composer name recognition, this
tool focuses on the features of the piece in a visual representation.
There are several limitations of this work. First, our dataset was
a limited set of 58 Classical pieces, with no pieces from the past 70
years represented. To see if this visual approach could help address
the issue of bias, we would need more diversity in terms of time pe-
riod, genre, instrumentation and composers (gender, race/ethnicity,
and country), as well as a mixture of well-known and lesser-known
composers. Second, the visuals capture information about time sig-
nature, key, etc. at the beginning of the piece, but these features
could change over time. Third, there is a tradeo between more
information and visual simplicity. Features that might be useful to
add to the visualizations could also result in visual clutter.
Finally, we found that measuring diculty computationally is a
very challenging task. We developed a diculty measure based on
our literature review and features identied as important during
our interviews, but our measure has limitations. It has not been
validated to see if the ratings would be consistent with expert
review. It is also dependent on the pieces in the dataset, since the
thresholds for levels were based on quintiles of the data as opposed
to xed thresholds. We are weighting all components equally, but
some factors could be more important than others. The diculty
factors may not be consistently meaningful across instruments
(i.e. a three-octave range would be much easier for piano than for
vocals), and instrument-specic diculty factors such as ngerings
are not included.
There are several challenges of implementing this project on a
larger scale. The biggest obstacle is obtaining pieces in symbolic
format, as we are currently limited to pieces that are not under copy-
right. Some manual review and editing of the Kern les was needed
to add missing information, a process which could potentially be
streamlined. Finding the pieces in Spotify was also a manual pro-
cess. Many of the pieces contained multiple metadata records in
addition to the title such as opus and movement numbers. Song
titles in Spotify could contain any combination of these elds and
could be in English or the original language of the composition.
As a result, identifying the correct songs and extracting their IDs
required some trial and error, listening to the recordings while
reviewing the scores.
To address some of the issues identied during testing, our fu-
ture work would include addressing cross-browser and mobile com-
patibility. We would also further develop the interface to include
features such as instrument lters and search capability. We would
explore dierent color schemes or other visual representations of
mood to ensure they are meaningful for people with color blindness.
Finally, we would expand our dataset to include a more diverse set
of pieces in terms of time period, instrumentation, and composer
To Cole Anderson, Michelle Ramirez, Ruvinee Senadheera, Duo-
Wei Yang and Dr. Anne Sullivan for assistance with user interviews
and user interface wireframes.
B W Andrews. 2020. Identifying the Characteristics of Levels of Diculty in
Educational Music: The Music Completity Chart (MC2). The Canadian Music
Educator 62, 1 (2020), 17.
Shih-Chuan Chiu and Min-Syan Chen. 2012. A Study on Diculty Level Recog-
nition of Piano Sheet Music. In 2012 IEEE International Symposium on Multimedia.
IEEE, Irvine, CA, USA, 17–23. https://doi.org/10.1109/ISM.2012.11
Arthur Flexer. 2014. On Inter-Rater Agreement in Audio Music Similarity. In
15th International Society for Music Information Retrieval Conference (ISMIR 2014).
ISMIR, Taipei, Taiwan, 6.
Jiajun Zhu and Lie Lu. 2005. Perceptual Visualization of a Music Collection. In
2005 IEEE International Conference on Multimedia and Expo. IEEE, Amsterdam,
The Netherlands, 1058–1061. https://doi.org/10.1109/ICME.2005.1521607
R. Khulusi, J. Kusnick, C. Meinecke, C. Gillmann, J. Focht, and S. Jänicke. 2020.
A Survey on Visualizations for Musical Data. Computer Graphics Forum 39, 6
(March 2020), 82–110. https://doi.org/10.1111/cgf.13905
Mats B Küssner. 2013. Music and Shape. Literary and Linguistic Computing 28, 3
(2013), 8. https://doi.org/10.1093/llc/fqs071
S. E. Palmer, K. B. Schloss, Z. Xu, and L. R. Prado-Leon. 2013. Music-color
associations are mediated by emotion. Proceedings of the National Academy of
Sciences 110, 22 (May 2013), 8836–8841. https://doi.org/10.1073/pnas.1212562110
Elias Pampalk and Masataka Goto. 2006. MusicRainbow: A New User Interface
to Discover Artists Using Audio-based Similarity and Web-based Labeling. In
Proceedings of the International Society for Music Information Retrieval. ISMIR,
Victoria, BC (Canada), 4.
Jeremy Sawruk and Jacob Walls. 2020. Personalized Sheet Music Search. In 7th
International Conference on Digital Libraries for Musicology. ACM, Montréal QC
Canada, 45–47. https://doi.org/10.1145/3424911.3425516
Markus Schedl, Arthur Flexer, and Julián Urbano. 2013. The neglected user in
music information retrieval research. Journal of Intelligent Information Systems
41, 3 (Dec. 2013), 523–539. https://doi.org/10.1007/s10844-013- 0247-6
Beau Sievers, Caitlyn Lee, William Haslett, and Thalia Wheatley. 2019. A multi-
sensory code for emotional arousal. Proceedings of the Royal Society B: Biological
Sciences 286, 20190513 (2019), 10. https://doi.org/10.1098/rspb.2019.0513
Véronique Sébastien, Henri Ralambondrainy, Olivier Sébastien, and Noël Con-
ruyt. 2012. Score Analyzer: automatically determining scores diculty level
for instrumental e-Learning. In Proceedings of the 13th International Society
for Music Information Retrieval Conference, ISMIR. ISMIR, Porto, Portugal, 7.
Siu-Lan Tan and Megan E Kelly. 2004. Graphic Representations of Short Musical
Compositions. Psychology of Music 32, 2 (2004), 191–212.
Lieven Verschael, Mark Reybrouck, Christine Jans, and Wim Van Dooren. 2010.
Children’s Criteria for Representational Adequacy in the Perception of Simple
Sonic Stimuli. Cognition and Instruction 28, 4 (Oct. 2010), 475–502. https://doi.
Martin Wattenberg. 2001. The Shape of Song. http://turbulence.org/project/the-